Lightweight anomaly detection for power grid microservices based on temporal-aware fusion and graph neural networks
TL;DR
A lightweight anomaly detection method integrating temporal-aware multimodal fusion and Graph Neural Network, which enhances fusion accuracy without complex pre-training and provides an efficient solution for the operation and maintenance of smart grid microservices.
Abstract
Microservice architecture is widely adopted in the digital transformation of smart grids, yet its distributed nature poses dual challenges for anomaly detection: the strong temporal periodicity of power grid data and the obscurity of fault propagation paths. This paper proposes a lightweight anomaly detection method integrating temporal-aware multimodal fusion and Graph Neural Network (GNN). Its core innovations are as follows: A lightweight temporal attention fusion strategy adaptive to power grid load periodicity, which enhances fusion accuracy without complex pre-training; A simplified GNN-based root cause localization method leveraging a compact power grid domain knowledge graph. Experiments on real power grid data demonstrate that the proposed method achieves an F1-score of 92.3% and a localization latency of 2.2 seconds, outperforming traditional methods by 2.5 - 3.0 percentage points in performance. Our research provides an efficient solution for the operation and maintenance of smart grid microservices.